NumPy_Matplotlib_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.asarray([1,2,3],dtype='float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(type(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3.]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n"
     ]
    }
   ],
   "source": [
    "print(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 0]\n",
      " [3 4 0]\n",
      " [3 8 0]]\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[1,2,0],[3,4,0],[3,8,0]])\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 3)\n"
     ]
    }
   ],
   "source": [
    "print(b.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  1   2   0]\n",
      " [  3 100 100]\n",
      " [  3 100 100]]\n"
     ]
    }
   ],
   "source": [
    "b[1:,1:] = 100\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[6 0 0 0 0]\n",
      " [0 6 0 0 0]\n",
      " [0 0 6 0 0]\n",
      " [0 0 0 6 0]\n",
      " [0 0 0 0 6]]\n"
     ]
    }
   ],
   "source": [
    "six_identity = 6*np.eye(5,dtype='int32')\n",
    "print(six_identity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 18, 19, 17, 19],\n",
       "       [11, 18, 16, 18, 12],\n",
       "       [10, 16, 18, 12, 11],\n",
       "       [14, 18, 19, 10, 13],\n",
       "       [18, 17, 19, 15, 12]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(10,20,(5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "a2 = np.random.randint(10,20,(5,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "a1 = np.random.randint(0,10,(5,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5]\n",
      " [7]\n",
      " [8]\n",
      " [1]\n",
      " [8]]\n"
     ]
    }
   ],
   "source": [
    "print(a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18 13 14 10 15]\n",
      "18\n",
      "10\n",
      "70\n",
      "14.0\n"
     ]
    }
   ],
   "source": [
    "print(a2)\n",
    "print(np.max(a2))\n",
    "print(np.min(a2))\n",
    "print(np.sum(a2))\n",
    "print(np.mean(a2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.exp?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "a3 = np.random.randint(10,20,(5,5,6)) #Tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 2 3 2]\n",
      " [4 2 1 4]\n",
      " [2 4 3 4]]\n"
     ]
    }
   ],
   "source": [
    "a = np.random.randint(1,5,(3,4))\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = a+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5 5 5 5]\n",
      " [5 5 5 5]\n",
      " [5 5 5 5]]\n"
     ]
    }
   ],
   "source": [
    "c = b - a\n",
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6  7  8 10]\n",
      " [ 6  7  8 10]\n",
      " [ 6  7  8 10]]\n"
     ]
    }
   ],
   "source": [
    "l = np.array([1,2,3,5])\n",
    "print(l+c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 25], dtype=int32)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  2  3  2]\n",
      " [ 4  2  1  4]\n",
      " [ 2  4  3  4]]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "b = np.array(a) #create a copy of a "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "b[0][0] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[20  2  3  2]\n",
      " [ 4  2  1  4]\n",
      " [ 2  4  3  4]]\n"
     ]
    }
   ],
   "source": [
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  2  3  2]\n",
      " [ 4  2  1  4]\n",
      " [ 2  4  3  4]]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = np.vstack((a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  2  3  2]\n",
      " [ 4  2  1  4]\n",
      " [ 2  4  3  4]\n",
      " [20  2  3  2]\n",
      " [ 4  2  1  4]\n",
      " [ 2  4  3  4]]\n"
     ]
    }
   ],
   "source": [
    "print(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = np.hstack((a,b))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[10  2  3  2 20  2  3  2]\n",
      " [ 4  2  1  4  4  2  1  4]\n",
      " [ 2  4  3  4  2  4  3  4]]\n"
     ]
    }
   ],
   "source": [
    "print(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(x+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5] [2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use(\"seaborn-bright\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXMAAAEPCAYAAACwWiQoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3deVhU5eIH8O/MwMwgckUSza5hoGlm6S3vT8M0IlzqJ4ihIEFgVzDXFhUEygW1m+GS92kRwaQUDEUzcknLzG6muV63UH8lbldBxAVlmYXl/P5AJ0BZxDlzZs58P8/T88icmXlfX+Hb4cx851UIgiCAiIhsmlLqCRAR0f1jmBMRyQDDnIhIBhjmREQywDAnIpIBhjkRkQwwzMlqHT58GBEREQgICIC/vz+io6Pxxx9/AACOHTuGN99802xjde3aFdeuXWvy/S9cuIBu3bohMDDQ9N/QoUOxbt06AMD69esxduzYRp9n+vTp+O2335o9b6LbHKSeANHdGI1GjB07FmlpaejevTsA4JtvvsGYMWOwfft2PPnkk/joo48knaNWq8U333xj+rqgoAD+/v544oknmvwcu3fvxsiRI8WYHtkZhjlZJZ1Oh+LiYpSVlZluGzp0KFq2bInKykocOHAAc+fOxaZNmxAfHw+tVovff/8dV69exQsvvABXV1fs2LEDhYWFeO+99+Dt7Y34+HhoNBqcPHkSV69exbPPPovp06fD0dGx1thr165FZmYmqqqq4OrqihkzZqBTp06Nzrldu3bo2LEjzp49W+v2S5cuITExERcvXoQgCBg2bBiio6OxePFiXL58GTExMZg/fz569uxplrUj+8TLLGSVWrVqhdjYWERHR8PPzw+xsbH46quv0LdvX6jV6jvuf/z4caxYsQIZGRlIS0tDixYtsHr1akRGRmLZsmWm+x09ehRpaWn49ttvkZubizVr1tR6nn379iE7OxurVq1CdnY2oqOjMWnSpCbN+dChQzh//vwdoRwTE4M+ffpg48aNyMzMxIYNG7B582ZMnjwZbdu2xcKFCxnkdN94Zk5W6x//+AeCg4Oxf/9+7N+/H8uWLcOyZctM16Vr8vX1haOjI9zd3dGiRQv0798fAODh4YGioiLT/V5++WU4OzsDAAIDA7F9+3a8+uqrpuM//fQTzp07h9DQUNNtN2/eRFFREVxdXWuNqdfrERgYCACorKxE69atsWDBArRv3950n7KyMvznP/9BWloaAMDFxQVBQUH4+eefMWTIkPtdIiIThjlZpYMHD+LQoUOIjo6Gr68vfH19MWXKFPj7+2PXrl1o3bp1rfvXPVt3cLj7t7ZKpTL9WRAEKJW1fzmtqqpCYGAgYmNjTV9fvnwZrVq1uuO56l4zv5uqqirU/fijqqoqVFRUNPg4onvFyyxkldzc3JCcnIwDBw6YbissLERJSQm6dOnS7OfdsmULjEYjDAYDvv76a/j6+tY63q9fP2zevBmXL18GAGRmZmLUqFHNHq9ly5bo2bMnVq1aBQAoLi5GdnY2+vbtC6D6fy4MdjIHnpmTVfL09MSnn36KxYsX49KlS9BoNHBxccH7778PLy8vFBYWNut5tVotwsLCcPPmTQwePBjDhw+vdbxfv34YM2YMRo8eDYVCgZYtW+KTTz6BQqFo9t9l4cKFmDNnDtavXw+j0YiAgAAEBQUBAAYOHIjY2FgkJiaiX79+zR6DSMGPwCV7ER8fj0cffRRRUVFST4XI7HiZhYhIBnhmTkQkAzwzJyKSAYY5EZEMMMyJiGRAkrcmFhYWSzEsEZFNc3d3qfcYz8yJiGSAYU5EJAMMcyIiGWCYExHJAMOciEgGGOZERDLAMCcikgGGORGRDDDMiYhkQLQwT0lJwciRIxEUFIS1a9eKNQwRkdXS5GfBbWd3tNnWCm47u0OTnyXaWKLU+ffu3YtDhw4hMzMTOp3OtJktEZG90ORnweX4G1BU6SAIgEr/X7gcfwMAYGgfYvbxRDkz/+WXX9ClSxdMnDgR48aNw/PPPy/GMEREVsv51GxsOuMHj1XnMHXPIgCAokoH51OzRRlPlDPz69evIy8vD0uXLsWFCxcwfvx4bN269b72USQishWXixR4Y9N8rDkdiidaH8Porn9enVDqL4gypihh7urqCi8vL6jVanh5eUGj0eDatWt44IEHxBiOiMgqCAKw5t8OmPmFFmW6lzHn7zMQ1zMJalW56T5V2g6ijC3KZZZevXph586dEAQBBQUF0Ol0cHV1FWMoIiKrcK5AgZD3nPDmp07o0qESv8RvxPS/L6oV5ILSCaWdZ4kyvihn5r6+vti/fz9GjBgBQRAwc+ZMqFQqMYYiIpJUZSWQ+q0jktZooFAAH0Tr8drAciiVg1Gc/zGcT82GUn8BVdoOKO08S5QXPwGJNnTm5hREJAc555SYkqzFoVwVBj5dgflj9PhrG/EitaHNKSTZaYiIyJbpjcDir9T4+Bs1XJ0FpLytw7C+FZDyPR4McyKie7DnhApTlmpwKk+FEJ9yzBmlh1v9J8wWwzAnImqC4jJg7ioNvvheDQ/3KqyZXgbfnpVST8uEYU5E1IjvDqgwbZkWl64rMHaIEXEjDWjpJPWsamOYExHV43KRAu+mafDNr47o9nAl0mJ06PVoldTTuiuGORFRHbXKPwYgPtSASUONUDtKPbP6McyJiGo4V6BATKoW/z7qgN5dK/DhOAO6dLDOs/GaGOZERGio/CP1zJqGYU5Edq9m+WdQrwokRYtb/hEDw5yI7Fbd8k/q2zoESlz+aS6GORHZJWst/zQXw5yI7EpxGTAnQ4MV26yz/NNcDHMishu3yz8Ft8o/8aEGOGulnpV5MMyJSPbqln8+j9HhaSst/zQXw5yIZEsQgDU/OWDmCtsp/zQXw5yIZOlsgQIxKVr8fMwBfR6rLv88+ld5nY3XxDAnIlmpWf5RKoGkaD1G2VD5p7kY5kQkG3Io/zQXw5yIbJ7eCHy4To1PNth++ae5GOZEZNNqln9G+pRjto2Xf5qLYU5ENkmu5Z/mYpgTkc3Zul+FuM/kWf5pLoY5EdmMWuUfD3mWf5qLYU5EVq9u+Sch1ICJMi3/NBfDnIismr2Vf5qLYU5EVqnidvlntQYqlf2Uf5qLYU5EVifnnBKTk7U4bIfln+ZimBOR1WD5p/kY5kRkFX49rsKUpVrk5ivtuvzTXAxzIpLUzVJg7iqWf+4Xw5yIJFOz/DPO34i4kSz/NBfDnIgsjuUf82OYE5HFsPwjHoY5EVkEyz/iYpgTkahY/rEMhjkRiea3s0pMWVpd/hncqwJJY/R46AGWf8TAMCcis2P5x/IY5kRkVjXLP6HPlyMxkuUfS2CYE5FZ1C3/ZE0vw/Ms/1gMw5yI7hvLP9ITLcyHDRsGF5fq3606dOiAefPmiTUUEVmQJj8LzqdmQ6m/gPyqv2HigXXIPuzF8o/ERAlzg8EAAEhPTxfj6YlIIpr8LLgcfwOo1OGL31/D1D2LUFrujOlDDmBceFeWfyQkyjs9T548CZ1Oh9GjRyMyMhKHDx8WYxgisjDnU7NxpuhBDPr2e4z+9+fo3joHR0b0RGKnIAa5xEQ5M9dqtYiKikJwcDDOnj2LMWPGYOvWrXBw4CV6IltVUQks3heCmfvnwEFZgSX9xmNstxQoFQIEPd9zKDVR0tXT0xMdO3aEQqGAp6cnXF1dUVhYiPbt24sxHBGJ7M/yz0IEeGzAkn4T0KHlRdPxKm0HCWdHgEhhvm7dOvz+++9ITExEQUEBSkpK4O7uLsZQRCSiWuWflgI+f+1HRGpCoRR0pvsISieUdp4l4SwJABSCIJi9W2s0GpGQkIC8vDwoFArExMTg6aefNh0vLCw295BEZGb1lX9qvpulStsBpZ1nwdA+ROrp2gV39/rbV6KEeWMY5kTW62YpMGeVBitvlX8WjtWz/GMlGgpzviJJRCZb9jsgbpkGl4tY/rE1DHMiwuUiBd5J02DDr454vGMlVkzT4anOLP/YEoY5kR0TBGD1Tw6YVWPnn0mBRjgyGWwO/8mI7NTZAgWmpmix85gDnulWgUVjufOPLWOYE9mZujv/zB+jR+QA7vxj6xjmRHaEO//IF8OcyA7ojcCidWp88o0arV0ELJusw1Bv7vwjJwxzIpmrW/6ZHalHa+78IzsMcyKZqlX+aVuFtTPK4NOD5R+5YpgTyRDLP/aHYU4kIwXXq8s/G/ew/GNvGOZEMlCz/KMzAu+8YsDEoSz/2BP+UxPZuDOXFIhJ/bP88+FYPTr/lW83tDcMcyIbVVEJpGx2xPw1LP8Qw5zIJh07U13+OXJahRf/Xo6kaAPas/xj1xjmRDakbvnnsyk6BDzD8g8xzIlsxu4cFaamVJd/XvEtR2IEyz/0J4Y5kZW7WQrMztAg/QeWf6h+DHMiK1az/DM+wIhpISz/0N0xzImsUN3yz8o4Hf7WieUfqh/DnMiKCAKQucMBs1ZqoTcC74YZMCGA5R9qHL9FiKzEmUsKxKRosfM3ln/o3jHMiSRWs/zjoAIWvK5HhB/LP3RvGOZEEmL5h8yFYU4kAZ2huvzz6QaWf8g8GOZEFrY7R4UpKVqcZvmHzIhhTmQhNcs/Hdux/EPmxTAnsoBv9zkg/rPq8s+EACOmjTSghUbqWZGcMMyJRMTyD1kKw5xIBCz/kKXxW4vIzGqWf7y7VeDDcXp0eohvNyRxMcyJzKSiEli6yRELslj+IctjmBOZQa3yz/+UIymK5R+yrCaH+dmzZ3Hu3Dl07doV7dq1g4LtBqJa5R83FwHLp+jgz/IPSaBJYZ6RkYFt27bhxo0bGDZsGM6fP4+ZM2eKPTciq1az/BPma8SsCAPLPySZJl3N27x5M7744gu4uLjgtddew5EjR8SeF5HVulEKTE3RYFhiC1RWAetmluFfExjkJK0mnZkLQvW1v9uXVtRqtXgzIrJi3+5zQNxnGhSy/ENWpklh7u/vj/DwcOTl5WHMmDEYMGCA2PMisio1yz/dO1YiI06Hniz/kBVRCLdPuxtx6tQp/PHHH/Dy8kLXrl3va9DCwuL7ejyRpQgC8OWPjkhM10BvBGKCjSz/kGTc3eu/lteka+ZZWVnIysrCSy+9hKSkJGRnZzf6mKtXr8LHxwe5ublNnymRFTlzSYERc5wweakWj3tU4qeFpXjrZQY5WacmhXlmZiamTp0KAEhJSUFmZmaD9y8vL8fMmTOh1XIbcbIdmvwsuO3sDtfv3JD2r4/w/BQtDueqsPB1Pb5O1LHFSVatSWGuVCqh0VS/yuPo6Njoe8yTkpIQGhqKtm3b3v8MiSxAk58Fl+Nv4NgFN3hn/4r4Xe9i0F+3YF/CakQOZIuTrF+TfmH08/NDWFgYevTogZycHLzwwgv13nf9+vVwc3ND//79kZqaaraJEolJefIDvLNnBhYciUUb7RWsHTACwz2/QtWVh3EN/lJPj6hRTX4B9MSJEzhz5gy8vLzw2GOP1Xu/8PBwKBQKKBQKnDhxAo888giSk5Ph7u5uug9fACVrsjtHhdjFF/DHjS4Y3XU5FvSJhZv2OgBAgAJXBt6QeIZE1Rp6AbTBMF+7di2Cg4OxaNGiOy6tTJkypdGBIyIikJiYiE6dOtW6nWFO1uBGKTDn1s4/Xn85h9T+o+H31x9r3adS+zCu9c+RaIZEtTUU5g1eZnnwwQcBAB07doRKpTLvrIgktHmvA+KX/1n+meGzA+1O/QrUeOu4oHRCaedZ0k2S6B406TLL6NGjkZaWZrZBeWZOUim4rkDCcg027a0u//xrvN5U/tHkZ8H51Gwo9RdQpe2A0s6zYGgfIvGMif7U7DPz21xcXPDDDz/A09MTylsv63t6eppndkQWULf8Mz3MgPF1yj+G9iEMb7JZjYZ5SUkJLly4gBUrVphuUygUWLlypagTIzKX0/nVO//8ksOdf0i+GgzzjIwMpKWlQaVS4a233sJzzz1nqXkR3bfbO//MX6OBowOw8HU9XuXOPyRTDYb5pk2bsHXrVpSUlGDatGkMc7IZx84oMTlZi6NnuPMP2YcGw1ytVkOtVsPNzQ3l5eWWmhNRs92x889UHfz7cOcfkr8mf2RQE7tFRJLZlaPClKVanLlUvfNPYqQBri2lnhWRZTT41sS+ffvC29sbgiBgz5498Pb2Nh1btGhRswflWxPJnG6UAnPSNUjfrkbHdlVYNFaP556slHpaRGbX7Abovn376n1g7969mz0hhjmZS83yz/iAcsSGcOcfkq9mh7lYGOZ0vwquKxC/XIPNdyn/EMnVfZeGiKxFU8o/RPaIPwJkM2qWf/o+XoFFY1n+IbqNYU5Wr6ISSN6kxoI1ajg6AIvG6hH+Ass/RDUxzMmqHTujxNvJWhw7o8JL/1OOpGgDHnTj2ThRXQxzsko6A7BwrRpLNqrxwF9Y/iFqDMOcrE7N8k/4C0bMimD5h6gxDHOyGjdKgdnpGmTcKv98NbMM/Vn+IWoShjlZhZrln4lDjSz/EN0jhjlJqmb554lHKpERp2P5h6gZGOYkCUEAVv3oiMSVGhjLgenhBoz3Z/mHqLn4o0MWV7f88+E4Pbza8+2GRPeDYU4WU7P8o3Zk+YfInBjmZBE1yz//27scH0Sx/ENkTgxzEpXOACxYq0ZyjfJPwDMVUk+LSHYY5iSaX35TYWoKyz9ElsAwJ7OrWf55hOUfIotgmJNZbdrrgPjPNLh6U4FJgQbEBBtZ/iGyAIY5mUXd8s+XCTr08GL5h8hSGOZ0X1j+IbIO/JGjZjudr8DUFC125Tjg2e7VO/+w/EMkDYY53bOKSmDJRjUWZrH8Q2QtGOZ0T46eVmLyUpZ/iKwNw5yapOzWzj+3yz9pMdU7/xCRdWCYU6N++a1655+zBUq86mfEzFdZ/iGyNgxzqldRCTAng+UfIlvAMKe72rjHAQnLWf4hshUMc6rl0rXq8s+3+1j+IbIlDHMCUF3+ydjuiNnpLP8Q2SL+qBLLP0QywDC3Y+UV1Tv/3C7/fDhOjzBfln+IbBHD3E6x/EMkL6KEeWVlJaZPn44zZ85ApVJh3rx58PDwEGMoukcs/xDJkyhhvmPHDgDA6tWrsXfvXsybNw/JycliDEWN0ORnwfnUbCj1F/BDYQjG7kzFmSsaln+IZEaUMB8wYACef/55AEBeXh7atGkjxjDUCE1+FlyOv4EinQaxe1Kx/P+i0ekvudgw6Rc849Nf6ukRkRmJds3cwcEBcXFx2LZtGz766COxhqEGOJ+ajfWnXsKkXZ+gUO+OaT2TkNgrEWqlO64hR+rpEZEZKQRBEPVVr8LCQoSEhGDz5s1o0aLFrduKxRySUF3+mTXve3x9Ngh/e+AQlvtE4ek2hwAAAhS4MvCGxDMkonvl7u5S7zFRzsyzs7NRUFCAsWPHwsnJCQqFAiqVSoyhqI6qquqdf2ana2A0/C+Sek/D5B6L4aj880XOKm0HCWdIRGIQ5cy8rKwMCQkJuHLlCioqKjBmzBgMGDDAdJxn5uKoW/75+OWv8bcro6Co0pnuIyidUPz4xzC0D5FwpkTUHA2dmYt+meVuGObmVbf8kxhpQPgL5VAoar+bpUrbAaWdZzHIiWwUw1zGjp5W4u1kLX47y/IPkdxZ/Jo5ia/MACzI0mDpJkeWf4iIYW6Ldh5TYWrKnzv/zIowoJWz1LMiIikxzG1IUQkwO12DVT9W7/yzflYZ+j3BnX+IiGFuM+ru/BMbbIQTd/4holsY5lau5s4/T3py5x8iujuGuZWqVf4pB2a8Wr3zjwO7V0R0FwxzK3Q6X4EpS7XYfZw7/xBR0zDMrUh5BZC8UY0Fa9XQ3Nr553b5h4ioIQxzK1Gz/DOkT3X5p11rno0TUdMwzCXG8g8RmQPDXEIs/xCRuTDMJVCz/OP5IMs/RHT/GOYWVrP880agATEs/xCRGTDMLYTlHyISE8NcZFVVQMb26vJPeQXLP0QkDoa5iFj+ISJLYZiLgOUfIrI0hrmZHclVYvJSln+IyLIY5mZyu/yTvNER7q4s/xCRZTHMzaBm+SfCz4iZLP8QkYUxzO9DUQmQuFKDL3ew/ENE0mKYN4MgAJv2OCB+uQbXiln+ISLpMczv0aVrCsR9psGW/dXln8x3WP4hIukxzJuI5R8ismYM8ybIzVNgakp1+adf9wosZPmHiKwMw7wBLP8Qka1gmNejZvnHv0855rH8Q0RWjGFeR93yz+cxOgxh+YeIrBzDvAaWf4jIVjHMwfIPEdk+uw5zln+ISC7sNszzr1bv/LNlvyN6eFZi9bs6POnJ8g8R2Sa7C3OWf4hIjuwqzFn+ISK5soswr1v+WTxOjzCWf4hIRmQf5iz/EJE9kG2YlxmA+Ws0WLqJ5R8ikj9ZhvnPt8o/51j+ISI7Iaswr1v++TqxDM92Z/mHiOTP7GFeXl6Od955BxcvXoTRaMT48ePh5+dn7mFqYfmHiOyd2cN8w4YNcHV1xYIFC3D9+nW8/PLLZg9zTX4WnE/NhlJ/Af+t6IUJ+9dh87GOLP8Qkd0ye5i/+OKLGDx4sOlrlcq8bRxNfhZcjr8BRZUOy05EI2bPQhir1JgTuA/Rr3Rj+YeI7JLZw9zZufqVxpKSErz55pt4++23zfv8p2ZDUaXD2eKOeH3nMvg+9CNS+78Oz3ZGXFPlmHUsIiJbIcoLoPn5+Zg4cSLCwsIQEBBg1udW6i8AAB5xOYdToZ3g5XIaCgUg6NkAIiL7ZfYwv3LlCkaPHo2ZM2fC29vb3E+PKm0HqPT/BQB0+svpWrcTEdkrpbmfcOnSpbh58yaWLFmCiIgIREREQK/Xm+35SzvPgqB0qnWboHRCaedZZhuDiMjWKARBsHi3vbCw+L4eX/PdLFXaDijtPAuG9iFmmh0RkXVyd3ep95hNhjkRkT1qKMzNfpmFiIgsj2FORCQDDHMiIhlgmBMRyQDDnIhIBhjmREQywDAnIpIBSd5nTkRE5sUzcyIiGWCYExHJAMOciEgGbCrMjxw5goiIiDtu//HHHzF8+HCMHDkSWVlZVjGnzz//HEOGDDF9cuTp06fv8mjzKi8vR2xsLMLCwjBixAhs37691nGp1qmxeUmxVpWVlUhISEBoaCjCw8Nx/vz5WselWqvG5iXFWt129epV+Pj4IDc3t9btUv781TcnKddp2LBhpnETEhJqHcvKykJQUBBCQkKwY8cO8w4s2IjU1FTB399fCA4OrnW70WgUBgwYIBQVFQkGg0EICgoSLl++LOmcBEEQpk6dKhw7dswi87ht3bp1wnvvvScIgiBcu3ZN8PHxMR2Tcp0ampcgSLNW27ZtE+Lj4wVBEIQ9e/YI48aNMx2Tcq0ampcgSLNWglC9JhMmTBAGDRoknDp1qtbtUq1VfXMSBOnWSa/XC4GBgXc9dvnyZcHf318wGAzCzZs3TX82F5s5M/fw8MDHH398x+25ubnw8PBAq1atoFar0atXLxw4cEDSOQFATk4OUlNT8corryAlJcUi83nxxRfx1ltvmb6uuf+qlOvU0LwAadZqwIABmDt3LgAgLy8Pbdq0MR2Tcq0amhcgzVoBQFJSEkJDQ9G2bdtat0u5VvXNCZBunU6ePAmdTofRo0cjMjIShw8fNh07evQonnrqKajVari4uMDDwwMnT54029g2E+aDBw+Gg8OdGyOVlJTAxeXPj4V0dnZGSUmJpHMCgCFDhiAxMRErVqzAwYMHzf8r1V04OzujZcuWd91/Vcp1amhegDRrBQAODg6Ii4vD3Llza21CLuVaNTQvQJq1Wr9+Pdzc3NC/f/87jkm1Vg3NCZDue0qr1SIqKgrLly/H7NmzERMTg4qKCgDir5XNhHl9WrZsidLSUtPXpaWltRZMCoIgYNSoUXBzc4NarYaPjw+OHz9ukbHz8/MRGRmJwMDAWvuvSr1O9c1LyrUCqs/uvvvuO8yYMQNlZWUApF+r+uYl1Vp99dVX2L17NyIiInDixAnExcWhsLAQgHRr1dCcpPye8vT0xNChQ6FQKODp6QlXV1eLrZXNh3mnTp1w7tw5FBUVwWg04sCBA3jqqacknVNJSQn8/f1RWloKQRCwd+9ePPHEE6KPe3v/1djYWIwYMaLWMSnXqaF5SbVW2dnZpl+/nZycoFAoTJd/pFyrhuYl1VqtWrUKGRkZSE9PR7du3ZCUlAR3d3cA0q1VQ3OSap0AYN26dfjggw8AAAUFBSgpKTHNq0ePHjh48CAMBgOKi4uRm5uLLl26mG1ss2/obCkbN25EWVkZRo4cifj4eERFRUEQBAwfPhzt2rWTfE6TJ09GZGQk1Go1vL294ePjI/r4NfdfXbJkCQAgODgYOp1O0nVqbF5SrNWgQYOQkJCA8PBwVFRU4J133sH3338v+fdUY/OSYq3uhj9/dzdixAgkJCTglVdegUKhwPvvv4/09HR4eHjAz88PERERCAsLgyAImDx5MjQajdnGZp2fiEgGbP4yCxERMcyJiGSBYU5EJAMMcyIiGWCYExHJAMOcZGnXrl0YOnQo9Ho9gOr3/AYEBKCgoOCO+6ampqJfv34wGAwNPuc///lP5OXliTJfovvFtyaSbM2fPx9lZWV49913MWrUKEycOBHPPvvsHfcLCAiAt7c3HnvsMQQFBUkwU6L7xzNzkq3JkycjJycHEyZMQN++fe8a5Hv37oWHhwdCQ0OxatUqAEBFRQVCQ0Oxc+dOXLlyBUOGDEF+fj4iIiKQm5uLgwcPIiQkBGFhYRg3bpxFP7eFqD4Mc5ItR0dHhISEYPfu3fWeca9duxbBwcHw8vKCWq3GkSNH4ODggIULFyIpKQmxsbGYNm0a2rdvb3rMDz/8gIEDByIjIwMjRozAzZs3LfVXIqoXw5xk6+LFi/jss88QGxuL2NhYFBcXmzYNSE5Oxo0bN/Dzzz9j5cqViIqKQklJCTIyMgAAHTp0wNNPP42rV6/iueeeq/W848aNw7Vr1zBq1Chs3bq13k/OJLIkXjMnWTIajQgPD8ekSZPg4+ODmJgYPPzww7U+Vz09PR15eXmIi4sDAOh0Ovj5+WHTpk04f/48kpKS8Pjjj+Ohhx5CVFQUIiIikJiYiF9//RW9e/dGly5dkJKSgvLyckyaNEmqvyoRABv+oC2ihg5Ju44AAACSSURBVCQlJaFXr16mD1hKTExEUFAQnnnmGfTp0wdA9SWW+fPnmx7j5OSEQYMG4csvv8SWLVvwySef4KGHHkJwcDB69+5tut+TTz6J+Ph4tGjRAo6OjpgzZ45l/3JEd8EzcyIiGeA1cyIiGWCYExHJAMOciEgGGOZERDLAMCcikgGGORGRDDDMiYhkgGFORCQD/w984+Sdldo1sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "#plt.show()\n",
    "plt.scatter(x,y,color='orange')\n",
    "plt.xlabel(\"X-Axis\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.title(\"Simple Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y,color='orange')\n",
    "plt.xlabel(\"X-Axis\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.title(\"Scatter Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAFFCAYAAAByn18nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3dZ2BTZd8G8CuzI2kKlSE80gpqW0URcSAi8LBRqCCbYqG0sgTZZSl7D0H2BrXsJSLDQRXxBUTAR0Bky96jK2mbeb8fUlIqUKAkORnX74s0Oc39P7RenPuM/y0TQggQEfkpudQFEBFJiSFIRH6NIUhEfo0hSER+jSFIRH6NIUhEfo0hSI/lzz//RFxcHGJiYtCoUSN8+OGHOHHiRKE/7+DBgxg6dKjj64SEBNy6deuxahw4cCCqVauGxo0bo0mTJmjUqBG6du2KmzdvAgBq1aqFQ4cOPVJd5DsYglRoJpMJnTt3xsCBA/Htt99i06ZNiImJQceOHWG1Wgv1mSdPnsTVq1cdX+/cudMptcbHx+Obb77Bhg0bsGnTJkRERGDEiBGFrot8h1LqAsh7ZWdnIzMzE1lZWY7X3nvvPWi1WlitVigUCqxduxZLliyBXC5H0aJFMWHCBJQsWRJjx47FgQMHYDAYIITA6NGjUbp0aUyfPh2ZmZkYNGiQ4zPbt2+P+fPnQy6XY+TIkbh8+TLMZjMaNmyILl264MKFC2jbti2eeeYZXLx4EcnJyShRokSBtVepUgWTJk266/VVq1YhOTkZcrkcxYoVw5AhQxAYGJivrnHjxjnvL5GkJ4gew+LFi0WFChVErVq1RL9+/cSaNWtEVlaWEEKII0eOiMqVK4tLly4JIYRYsmSJGDJkiPjjjz/Exx9/LKxWqxBCiHnz5onOnTsLIYRYt26d6NSpk+PzIyMjxc2bN4UQQsTFxYmUlBQhhBA5OTkiLi5ObN68WZw/f15ERkaKvXv33rPGAQMGiIULFzq+zs7OFr169RIjR44UQghRs2ZNcfDgQbFr1y5Rp04dx3jr1q0T77zzjrDZbHfVRb6DR4L0WDp06IAWLVpg79692Lt3LxYsWIAFCxZg7dq12L17N95++22UKlUKgH1KeltoaChWrlyJ8+fPY8+ePdBoNAWOk5WVhb179yI9PR3Tpk1zvHb06FFUqFABSqUSFStWvO/3f/HFF9i4cSMAwGq14vXXX0efPn3ybfPrr7/i3XffRVhYGACgadOmGDNmDC5cuPDIfy/kPRiCVGj79+/H//73P3z44YeoWbMmatasiT59+qBRo0bYuXMnFAoFZDKZY/ucnBxcvHgR58+fx5gxY9ChQwfUrl0b5cqVcwTU/dhsNgghsHLlSgQFBQEAbt26hYCAAKSmpkKtVkOpvP+vc3x8PBITEx84xr8JIWCxWAr8PvJuvDBChRYWFoY5c+Zg3759jteuX78OvV6PyMhIVK5cGbt378a1a9cAACtXrsSkSZOwc+dO1KxZE7GxsXjxxRexbds2x4UUhUKRL3Ruf63ValGxYkUsWbIEAJCRkYE2bdogJSXFaftTrVo1bNmyxXE1et26dShSpAgiIiLuqot8B48EqdDKli2LWbNmYerUqbhy5QoCAgIQEhKCsWPHoly5cgCApKQkfPjhhwCA4sWLY+zYsdDr9ejbty9iYmJgsVhQtWpV/PDDD7DZbKhYsSJmzZqF7t27Y+bMmWjQoAHi4uIwY8YMTJ48GaNGjUJMTAxMJhMaNWqE9957z2nT1apVqyI+Ph7t27eHzWZDWFgY5s2bB7lcfldd5DtkQrCVFhH5L06HicivMQSJyK8xBInIrzEEicivMQSJyK951C0y169nSl0CEfmg4sVD7vsejwSJyK8xBInIrzEEicivMQSJyK8xBInIrzEEicivMQSJyK8xBInIrzk9BM1mM5KSkhAbG4vmzZsjJSUFhw8fRrVq1RAXF4e4uDhs2bLF2cO61B9/7MOwYYPyvTZs2CCYzWaJKiIiZ3H6EyMbN25EkSJFMGnSJKSmpuL9999Ht27d0KFDByQkJDh7OMmMGMEVx4h8gdNDsEGDBqhfv77ja4VCgb/++gunT59GSkoKIiIiMHjwYGi12kf+7IBLyxF4aakzy0VO6Q9gLB37yN/XvHkMli1bi8mTx0GlUuHKlcu4efMGBg8ejqioaPz00zasWrUMcrkcFSpURNeuH+PatauYPHk8TCYjMjLSER/fEdWr/xdxcS1RpkwEVCoVRowY69T9I/IlQXNnQvHPKegnTnXaZzp9OqzRaKDVaqHX69GjRw/06tULFSpUQP/+/bFs2TKUKVMGs2bNcvawknryyVKYMmUmmjVrhY0b1yMjIx2LF8/DtGlzMGfOIty4cQ179/6Gs2fPoHXrtvj889no3bs/1q9fDcC+fm98fCIDkKgAyt/3QDP8U+d/rtM/EcDly5fRrVs3xMbGIiYmBhkZGdDpdACAunXrYtSoUYX6XGPp2EIdtbnac89FAQBKlCiJQ4cO4MKF80hLS0W/fj0A2JeGvHjxIipUqIgvv1yEzZu/ASDLt3BPePjTElRO5CUMBui6d4LtP0/BMGSEUz/a6UeCN27cQEJCApKSktC8eXMAQGJiIg4ePAgA2L17N8qXL+/sYSV157KSAFCq1H9QokRJfP75bMycOR/Nm7dC+fIvYuHCuWjQoCGGDBmFSpVeK/AziCiPduQQKM6cRub0ORAhOqd+ttOPBOfOnYuMjAzMnj0bs2fPBgAMHDgQY8eOhUqlQrFixQp9JCil33/fg8TEOMfXBV0ZLlq0KFq1aovu3TvBarWiVKnSqFWrLmrWrI1p0yYjOXkJSpQoibS0NHeUTuTVVNt/QtCShcjq3A3mqtWc/vketdoc+wkS0Z1k6WkoWv1NiJAQpP64AwgKKtTnFNRP0KOaqhIR3Uk7uD/k164i7cvlhQ7AB+ETI0TkkdSbNiJwzUpk9eoHS8VKLhuH02Ei8jiya9cQVqMyrP8pg7StKYBK9Vifx/b6ROQ9hEBIv56Q6fXInDnvsQPwQRiCRORRAlYtR8B3m2EYNBTW6OddPh6nw0TkMeQXzqNojSqwlH8R6V9vBhQKp3wup8OP6Y8/9qFRo7ro3r0TPv64Mzp1isfatSvvue3ly5fQqVO8W+o6ceIYlixZcN/3t2z5FnPmzHBLLUSPzWZDSM9ukFmtyJw+x2kB+CC8ReYhvfrqa47OMSaTCbGxzVC/fkOEhNz/XxhXe+65KMcje0TeLnDxfKh/3Y7MydNge7qs28b1qhAMWLUcgSuc3EWmzQcwtnq055GzsrIgl8vxzz8nMXfuTCgUCqjVavTvn/dw97lzZzFq1BAsWPAVAGDo0EFo0+YDjB8/ChUrVsKpUycBAOPHT4FWq8WMGVNx8OCfAIC6dRugZcs2GDNmOJRKJa5cuQyz2Yzateth584duHr1CsaPn4KrV6/gm2/WYcSIcVi3bhV++eVnWCwWaLVajBkzyUl/Q0Supzh1AtpRw2CsXRc5cfFuHdurQlBK+/fvQ/funSCXy6FUKtG7dxKmT5+CgQM/xXPPReHXX7dj5swp6NatFwAgPDwCAQGBOH36HzzxxBO4fPkinn++PAwGA+rUqY/evftjxIhP8dtvOxEUFIzLly9h/vwvYLVa0bVrIl599XUA9g41AwZ8ikmTxuLy5YuYPHk6Fi2ah507d+DZZyMBADabDenp6fj889mQy+Xo06c7jhw5LNnfFdEjsVgQ0r0zREAA9FNnAm5+jt6rQtDYKvaRj9qc5c7p8G3jx492TEdffrkS5s6dme/9mJgm2Lr1W5Qs+STq1XvX8XpkZF7XGZPJhGvXruLllytCJpNBqVSifPmXcObMP7nbRgMAtNoQREQ8DQAICQmB0WhyfJ5cLodKpcLw4Z8gKCgI165dy9ehhsiTBc/8HKr9+5AxbzFsT5Zy+/i8MPIYihUrjpMnTwAA/vzzD5QpE57v/f/+tzZ+/30PduzYjvr137njnfz/0kVElHVMhS0WC/766yCeesr+WQ/TXebkyRPYsWM7Ro4ch969+0MI22PsFZH7KA4dRPCkcchp3BTG95tLUoNXHQl6mgEDPsHUqRMhhIBCocDAgUPyvR8QEICKFV9BamoqdLrQ+35O1arV8L//7Ufnzh1gNptRq1YdREVFP3QdTz1VBkFBQUhMjINarcITTxTDjRvXC71fRG5hNNp7BBYNg37CZ5KVwfsEXWzy5PGoWbO24xwfEdlpRg1D8IypSF+2Gqa6DVw6Fu8TlEjv3t1gNOYwAIn+Rfn7HgTNmobsD9q7PAAfhEeCROReBgPCar4F2GxI3b4LQuv6e23ZT5CIPIZ25BDIz55B+teb3RKAD8LpMBG5jernFAQtWYjsTh/B/NbbUpcDgNNhInITWVoqitao8tit8guD02Eikpw7WuUXBqfDRORy6k0bEbh2FbJ6J7m0VX5hcDpMRC7laJX/VDjStmxzeafoe+F9gkQkDTe3yi8MhiARuUy+VvmP8CioO3E6TEQu4WiV/+JLSF+/yW2dou+F02Eici+bDSE9PwJsNre2yi8M3iJDRE5nb5X/CzI/mw5bbh9MT8XpMBE5leLkCRSt/TZMb72NjOVr3d4p+l44HSYi97BYENK9k2St8guD02EicprgGVOh+mM/MuYvkaRVfmHwSJCInEJx6CCCJ49HTpOmMDZpJnU5D43nBIno8RmNKFqvBmQ3byJ1x28QYU9IXVE+bKBARC6lmTgWyiN/I335Go8LwAfhdJiIHotyz28Imvm5vVV+nfpSl/PIOB0mosLT6xFWq6pbW+UXBqfDROQSntYqvzA4HSaiQlH9nIKgLxYhu3M3j2mVXxicDhPRI8vXKn/br0BgoNQlFYjTYSJyqnyt8j08AB+E02EieiTqb7/x2Fb5hcHpMBE9NNm1awir/gasZSIka5VfGGygQESPTwiE9OsBmcHgsa3yC4MhSEQPxd4qfwsMg4d5bKv8wuB0mIgeSH7+HIr+9y17q/yvNwNy7zp+cuvVYbPZjMGDB+PixYswmUzo2rUrnn32WQwcOBAymQzPPfcchg0bBrmX/SUS+a1/t8r3sf93nR6CGzduRJEiRTBp0iSkpqbi/fffR3R0NHr16oXKlStj6NChSElJQd26dZ09NBG5QNCieVD/3w5kTpnh8a3yC8Ppkd6gQQP07NnT8bVCocDhw4fxxhtvAACqV6+OXbt2OXtYInIBxckT0IwaBmPd+shp207qclzC6SGo0Wig1Wqh1+vRo0cP9OrVC0IIyHLbbGs0GmRm8twfkce73So/KAj6KTO8olV+Ybhkcn/58mW0a9cOjRs3RkxMTL7zfwaDATqdzhXDEpET3W6Vr58wBbaST0pdjss4PQRv3LiBhIQEJCUloXnz5gCAF154AXv27AEA7NixA6+99pqzhyUiJ1IeOoDgSeO8rlV+YTj9FpnRo0dj69atKFeunOO1Tz75BKNHj4bZbEa5cuUwevRoKO6xGDNvkSHyAB7eKr8wCrpFhvcJElE+mpFDETzzc6QvX+OVnaLvhY/NEdFDUe75DUGzpiE7Lt5nAvBBeCRIRHaOVvkCqdt3em2n6HthP0EieiBHq/wNW3wqAB+E02EiguqnbXmt8qtUlboct+J0mMjPydJSUbT6mxChoUj9cYfXd4q+F06Hiei+tIOSIL9xHWnJK30yAB+E02EiP6b+9hsErlttb5X/8itSlyMJToeJ/JTs6lWE1ajsda3yC4P3CRJRfj7aKr8weE6QyA8FrFyGgO+3Qj9yrE+1yi8MToeJ/Iz8/DkUrVEFlgovI339Jp/rFH0vnA4Tkd3tVvlCIHPabL8IwAfhdJjIj/h6q/zC4HSYyE8oThxH0dpvw1StBjKWrvbZTtH3wukwkb+zWBDycWefb5VfGJwOE/mB4OlToPpjPzIWfOHTrfILg0eCRD5OeegAgiePR877zWBs3FTqcjwOzwkS+bLbrfJv3bK3yi8aJnVFkmADBSI/pZkwBsojfyN9+Rq/DcAH4XSYyEcpf9vtd63yC4PTYSJfpNcjrOZbgIDPtcovDE6HifyMdsQQyM+dRfo3W/0+AB+E02EiH6P6aRuCvlyE7C7dYX7zLanL8XicDhP5EH9olV8YnA4T+QntwH5+3Sq/MDgdJvIR6m83IHD9GmT16e+3rfILg9NhIh/gaJUfHoG0zb7dKr8w2ECByJfla5U/nwH4iHhOkMjL5WuVHxkldTleh9NhIi/mj63yC4PTYSJfZLMhpEdXe6v86XMYgIXE6TCRlwpaOBfqnb8ic+pM2MIjpC7Ha3E6TOSF/LlVfmFwOkzkS9gq36k4HSbyMmyV71w8EiTyIqqftiF40ji2yncihiCRl1AeOgBdYjtYo1+AfvI0qcvxGQxBIi8gP38OujbNIYoUQfqKtRAhOqlL8hk8J0jk4WRpqQht0wyynBykrd0I25OlpC7JpzAEiTyZ0QhdfFsozpxG+qqvYY1+XuqKfA5DkMhT2WwI6dEF6l3/h4y5i2CuWk3qinwSzwkSeSjN6OEI/Hod9J+OgLFpC6nL8VkMQSIPFLhoPoJnfo7s+ERkf9xL6nJ8mstC8MCBA4iLiwMAHD58GNWqVUNcXBzi4uKwZcsWVw1L5PXUWzdD+0l/GBu8C/24yXwixMVcck5wwYIF2LhxI4KCggAAf//9Nzp06ICEhARXDEfkM5T790LXJQGWiq8gY+5iQKGQuiSf55IjwfDwcMyYMcPx9V9//YXt27ejbdu2GDx4MPR6vSuGJfJq8n9OIfSDlrCVKIn05NVAcLDUJfkFl4Rg/fr1oVTmHWRWqFAB/fv3x7Jly1CmTBnMmjXLFcMSeS3ZjRso0ropIATSV62HKF5c6pL8hlsujNStWxcvvvii489///23O4Yl8g5ZWQiNawn5lctIT14Fa7lnpa7Ir7glBBMTE3Hw4EEAwO7du1G+fHl3DEvk+axW6LokQvnHfmTMWQTL65WlrsjvuOVm6eHDh2PUqFFQqVQoVqwYRo0a5Y5hiTybENB+0h8B321G5tiJMDWMkboiv8TO0kQSCZo5DdqRQ5DV9WMYRoyRuhyfxs7SRB4m4Ou10I4cgpzGTWEYxpmRlBiCRG6m2vV/CPm4C0xVqiJzxlyuEicx/u0TuZHi2FHo2sfCGvE0Mr5YBgQGSl2S32MIErmJ/MplhLZpBhEQgPQV6yCKhkldEoGttIjcQqbPhC62BeS3biFt41auE+xBGIJErmY2Q5fYDsojh5G+bDUsFSpKXRHd4aGnw2fOnMEvv/yCK1euwIPuqiHybEJAm9QL6p9ToJ88DeZadaWuiP7loY4Ely5dih9//BHp6elo0qQJzp07h6FDh7q6NiKvF/zZBAQtT4ahT3/ktG0ndTl0Dw91JLh582Z88cUXCAkJQXx8PA4cOODquoi8XsCKpdBMHIucVrHIGvCJ1OXQfTxUCN6e/spymzuq1WrXVUTkA1Q/pyCkbw+YatRE5mfT2RjVgz3UdLhRo0Zo27YtLl26hI4dO6JOnTqurovIaykOHYQuIQ7WqOeRsTgZ4EGDR3voZ4dPnjyJEydOoFy5coiKinJJMXx2mLyd/MJ5FHmnNqBQIG1rCmylSktdEsEJzw6vXr0aq1evxjvvvIMJEyZgw4YNTiuOyFc4FknPzkb6inUMQC/xUCG4YsUK9O3bFwAwb948rFixwqVFEXmd24uk/3MKGV8sg/X5F6SuiB7SQ50TlMvlCAgIAACoVCrHBRIign2R9J5d7Yukz1kI89vVpa6IHsFDhWDt2rURGxuLChUq4PDhw6hVq5ar6yLyGpoxIxC4fi30nw6HsVlLqcuhR/TQF0aOHDmC06dPo1y5coiOjnZJMbwwQt4mcPEChAzsi+z4ROgnTOGtMB6qoAsjBYbgmjVr0KJFC3z22Wd3TYH79OnjvApzMQTJm6i/2wJdfCxMdesjY8kyQMlH8T1VQSFY4E/tySefBABERERAwUWgiRyU+/dC17kDLC9XtC+SzgD0Wg81HU5ISMDixYtdXgyPBMkbyE//g6IN60BotEjdksI1gr1AoY8EbwsJCcG2bdtQtmxZyHNbgZctW9Y51RF5EdmNGwht3RSw2ZC+ch0D0Ac8MAT1ej0uXLiAL7/80vGaTCbDV1995dLCiDxOVhZC41pBcfkS0tZ+C+szz0ldETlBgSG4dOlSLF68GAqFAj179kT16rz/ifyU1Qpd1w+h/GMfMhYvheUNLpLuKwp8YmTTpk347rvvsGrVKh75kf8SApohAxGwdRMMo8dzkXQfU2AIqtVqqNVqhIWFwWw2u6smIo8SNGcmghfOQ1aX7sju2FXqcsjJHrq9Plvqkz8K2LAO2uGf2BdJHz5a6nLIBQq8Reatt95ClSpVIITAb7/9hipVqjje++yzz5xeDG+RIU+i2r0ToS0aw1zpNaSv3sA1gr1YoZ8Y+f333+/7jW+88cbjVXUPDEHyFIrjx1CkYV3YSpRA2qYfuEawlyt0CLobQ5A8gfzqFRR5tw5kOTlI3ZrCNYJ9wGPfLE3kL2T6TOjatoT85k2kfbOFAegHGIJEt5nN0H3YHsrDh5CRvBKWl1+RuiJyA4YgEWBfJL1/b6h/2obMKTNgqlNf6orITR76FhkiXxY8ZSKCln0FQ58k5HzQXupyyI0YguT3AlYug2bCGOS0bIOsAZ9KXQ65GUOQ/Jrq5xSE9PkYpuo1kTllBjtD+yGGIPktxV+HoEtsB2tkNDKWcJF0f8UQJL8kv3gBobHNIXQ6pK9YCxGik7okkgivDpPfkaWn2RdJNxiQ9u33XCTdzzEEyb/cXiT91Emkr1wP6wvlpa6IJMYQJP9hsyGk50dQ7/wVGbMXwFythtQVkQfgOUHyG5qxIxG4fg30nwyDsXkrqcshD8EQJL8Q+MUiBE+fgux2Ccju4fw1s8l7MQTJ56m/3wrtwL4w1msA/fjJvBeQ8nFZCB44cABxcXEAgLNnz6JNmzaIjY3FsGHDYLPZXDUsUT7KP/ZB1ynevkj6vCVcJJ3u4pIQXLBgAT799FMYjUYAwLhx49CrVy8sX74cQgikpKS4YliifOSn/0HoBy1hK14S6cmrAY1G6pLIA7kkBMPDwzFjxgzH14cPH3Z0oq5evTp27drlimGJHGQ3byK0TTPAarUvkl6ihNQlkYdySQjWr18fyjumHUIIyHLPw2g0GmRmsoM0uVB2tn2R9EsXkZ68GtZnuUg63Z9bTpDI5XlZazAYoNPxESVykduLpO/fi4xFyVwknR7ILVeHX3jhBezZswcAsGPHDrz22mvuGJb8jRDQDB2EgC3fwjBqHEyN3pO6IvICbgnBAQMGYMaMGWjVqhXMZjPq12fXXnK+oLmzELxgLrI6d0N2p4+kLoe8BFebI58Q8M166DrGwxjTBBkLvgDkvAWW8hS02hx/U8jrqX7bhZBunWCuXAUZs+YzAOmR8LeFvJri+DHo2rWGNTwC6V+tAAIDpS6JvAxDkLyW7OpV+72AKjXSV6yDKBomdUnkhfgMEXknvR6hbVtAfvMG0jZsgS3iaakrIi/FECTvY7FA1/GORdIrVpK6IvJinA6TV5Glp0EX1woBKT9CP3EqF0mnx8YjQfIaihPHoWvXGoqzZ5A56XPkxMVLXRL5AIYgeQX1j98hpMuHQIAa6es3wfzmW1KXRD6C02HybEIgaNpn0H3QCtay5ZD6wy8MQHIqHgmS5zIYENK7GwI3rEdO0+bInDITCA6WuiryMQxB8kjy8+egax8L5eFD0A8ZiezuPdkWn1yCIUgeR7Xr/6BLjAPMFmQsXwNT7XpSl0Q+jOcEyXMIgcAlCxHa/D3YioYh7fufGIDkcjwSJM9gMkE7KAlByUtgrFsfmXMWQuhCpa6K/ABDkCQnu3YNoQkfQPX7bzD06oesAZ8ACoXUZZGfYAiSpJR//gFdfFvIU28hY/4SGJs0k7ok8jM8J0iSCVi7CkXeawAoFEjd9CMDkCTBECT3s1qhGf4pdB91hLnSa0j9fjusL1WQuiryU5wOk1vJ0lKh65wA9c8pyE7oCP2o8YBKJXVZ5McYguQ2iuPHoItrBcWF88j8bDobIJBHYAiSW6i/34qQrh8CQUFIW78ZlspvSl0SEQCeEyRXEwLBUyfZ1wF55lmk/rCdAUgehUeC5DoGA0J6foTAjV8jp3krZH42HQgKkroqonwYguQS8rNnENo+Foqjf0M/fAyyu3ZnAwTySAxBcjrV/+2A7sN2gNWG9OVrYa5VR+qSiO6L5wTJeYRA4KJ5CG3RGLZixZH2/U8MQPJ4PBIk5zAaoR3YF0HLvoKxwbvInDUfIkQndVVED8QQpMcmv3oFug4fQLXvdxj69EdW/8GAnJMM8g4MQXosyv/ttzdASE9D+qKvYIppInVJRI+E/1xToQWsWm5vgKBSIXXzNgYgeSWGID06iwWaoYOh+7gLzK9XtjdAKP+i1FURFQqnw/RIZKm3oOvUAepffkbWh51hGDGWDRDIqzEE6aEpjh5BaLvWkF+6iMzPZyEnNk7qkogeG0OQHop6yyaEdOsEodEg7evNsLxeWeqSiJyC5wSpYDYbgiePR2h8LKyRkUj78RcGIPkUHgnS/en10H3cBQGbNyKnZRtkTp4GBAZKXRWRUzEE6Z7kZ04jtH0bKI4dhX7UOGR3+ogNEMgnMQTpLqod26Hr2B4QAumrvoa5Rk2pSyJyGZ4TpDxCIGj+bIS2eh+2kk8i9fvtDEDyeTwSJLucHIT0743AlctgfKcRMmfNg9CGSF0VkcsxBAnyK5eh69AWqv37YEgahKy+A9gAgfwGQ9DPKffvtTdAyMxE+pJlMDWMkbokIrfiP/d+LGDlMhRp/A4QGIjULdsYgOSXGIL+yGKB5tMB0PXoCvObVZH6w3ZYXygvdVVEknDrdLhJkyYICbGfbH/qqacwbtw4dw5PAGS3bkLXsQPUv25HVuePYBg2GlDyrAj5L7f99huNRgBAcnKyu4akf1H8fRih7dpAfvUyMqbPgbF1W6lLIpKc20Lw6NGjyM7ORkJCAiwWC/r06YOKFSu6a3i/p960EbrunWELCUHahlrcvkwAABLzSURBVC2wvPq61CURFUhmugGl/igUhmNQGI5CaTgOheEozEWqILPCF04bx20hGBgYiMTERLRo0QJnzpxBx44d8d1330HJqZhr2WwInjQOms8mwPzqa8hYsgy2J0tJXRWRnRCQGy9BYTgGpeEoFPpjjj/Lzbccm9kUWlg1kTCH1YSxpHM7mLstgcqWLYuIiAjIZDKULVsWRYoUwfXr11GqFP+HdBWZPhMhH3VCwHebkd3mA+gnTGEDBJKGsEKefc4edIbjuYFn/7PcmunYzKYqCqsmGsYS78GqiYJFEwWrNhq2gP+47Nl1t4Xg2rVrcfz4cQwfPhxXr16FXq9H8eLF3TW835Gf/sfeAOHEcWSOnYicxM5sgECuZzNDkfVP7vTVPo21h95xyGw5js2s6idh1UbBWLqNPeg00bBooyFUxdz+eyoTQgh3DGQymTBo0CBcunQJMpkM/fr1Q6VKlfJtc/165n2+mx6FavtP0HWKB2QyZCz8CuZqNaQuiXyNNRuKrJO55+zyztcpsk5BJix5mwWG54ac/YjO/udICFVRt5ZbvPj9HwF1Wwg+DIbgYxICQfNmQTP8U1ijnkf6l8the7qs1FWRF5NZMnMvTByzH9np7Ud48uwzkMEeHQJyWIPL2YNOEw2LNip3KhsJKDQS74FdQSHIqxK+IicHIf16InD1ChgbNUbG9DmAVit1VeQlZKabudPXY3lTWf0xKIwXHdsImRpWzbMw616BtVRre9Bpo2ENfgaQB0hY/eNhCPoA+eVL0MXHQvW/P2AY8AmyeiexAQLdTQjIjVfyB53hGJT6o5Cbb+RtJg+GRRMFc1g1ZOce3Vk1kbAGlQXkvhcZvrdHfka5dw90HT6AzGBA+pcrYHqnodQlkdSEDfKc87nn647nBl7ulVhLumMzm7IIrJpIGIu/C6vWHnQWTTRsgU8BMv/5R5Qh6MUClydD2783bKX/g7S1G2GNfl7qksidbBYosk/fcY/dnVdis/I2UxeHRRMN45MtHLecWDVRsKlL8o4BMAS9kvziBQRPGoeg5ckw1aiJjPlLIIqGSV0WuYrNCIXhZO7RXN4UVpF1EjJhdmxmDXwKVk0UsovG2y9Q3L4Sq35CwuI9H0PQi8jPnEbwjKkIXLkMEAJZ3XvBMHgoGyD4CovecauJ45YTwzEosk5DBhsA+5VYW1AELJpomIo3gEUT6ThnJ5TsBF4Y/L/HCyhOnkDw55MRsG41oFAg54P2yOreC7Yy4VKXRoUgM9+yT1v1x/Kdr1PknHdsI2QqWIOfgVX7Eowlm+XeYxcNa/CzgIJP/TgTQ9CDKY78jeCpExHwzddAYCCyP+yM7G49+eyvNxACMtM1x1MTSn3e42Jy07W8zeRBsGgiYS5SBTmaePstJ5qo3CuxKgl3wH8wBD2Q8uCfCJ4yCQFbvoVNo0V2917I6tIdgo8Zeh5hgzznQt7NxLfP1xmOQW5Jc2xmU+pg1UTBWKyeY/pq0UbDFhjuV1diPRFD0IMo9/2O4CkTEbDtB9h0oTD06Y/sTl0hwnhiW3I2CxTZZ+54euJo7n+PQ2Y15G2mKgaLJgrGJ5s5bjmxaqJgCyjFK7EeiiHoAVS7/g/BUyZBveNn2MLCYBg0BNmJnSB0oVKX5n9sRiiyTuU7orMf4Z2ATJgcm1kDStuvxJaOy73lxH41lldivQ9DUCpCQPXLzwieMhHq33bBVrwE9MNGI7t9Ah93cwerIfcK7LHcCxS5HU+yT0MmrAAAAVneldgn6uSer4uENTgSQsV/oHwFQ9DdhID6x+8QPHUSVPv3wVqqNPRjJiD7g3ggKEjq6nyOzJyW73ydvQHAcShyzjq2ETJl7pXYF2As+f4dz8Q+Byj4M/F1DEF3sdmg3vytPfz+OghreAQyJ32OnNZtgQDvffjcIwgBmfnGHdPX3Pvr9MegMF3J20weCGvwczAXeR05mg9yz9dFwxpcFpCrJdwBkhJD0NWsVgR8sx7Bn0+G8ugRWMo9Y1/kqFlLQMVbIB6JEJAbLzraOeUd4R2F3Jzq2MymCLG3Yi9WO7cBgL1DsS0oApApJNwB8kQMQVcxmxGwbrU9/P45BUtUNDLmLISxcVM+4fEgwgp59tl8/etudyiWW/WOzWyqMPszsSWawKqNcnQotgWU5pVYemj8v9HZjEYErlqO4OlToDh3FpbyLyF9UTJMDWPY3urfbKb8rdhvn6/LOg6ZzejYzBpQClZNFHJKt83XuFOoed8kPT6GoLNkZyNw2ZcInjkNiksXYX6lEvRjJsJUrwGPSqxZUBpO3LV0oiLrn3+1Yo+ARRsF0xM18xbZ0URCqIpIWDz5Oobg49LrEfTlYgTPng759WswV66CzKkzYf5vLb8LP5klw3FBwvG4mOEY5Nln81qxyxSwBpWDVZu3opg98J7zmFbs5F8YgoUky0hH0OIFCJo7E/Jbt2Cq9l9kzV8C81tv+3z42VuxH73jlpPc20+Mlxzb2FuxR8KsqwRrqdjcdSeiYQ0u59Wt2Mn3MAQfkSz1FoLmz0HQwnmQp6fBWKcesnonwfJ6ZalLcy4hIDdevuOWk+O5gXcUcvPNvM0UGnsDgLAa/2rF/rRPtmIn38Pf0ocku34dwfNmIXDRfMgNehjfaYSsPkmwvPyK1KU9HmGDPOfcfVqxZzg2symL5E5hG91xvi4atsD/sAEAeTWG4APIr15B0MxpCPpqMZCTA2Pj95HVsx+s5V+UurRHYzPntWLPXSs2rxV7tmMzq7qkvdtJqVZ5i2Jrou1XYn18mk/+iSF4H/IL5+1dnJcnAxYLjE1bIKtXP1ifi5S6tIJZc+yLYueuOZF/Uew7W7GXyW3F/nbuLSfRkiyKTSQ1huC/yM+cRvD0KQhctRwAkNMqFlkf94atbDmJK8vPvij2Ha3Ycx8ZU2SfydeK3RpcFlZNFEzF3809sstdFFvJJg1EAEPQQXHiuL2F/fo1gFJpb2H/cW/YniojaV0y8627bjlRGI5BkXPBsY2QqWDVPAeL7mUYS7XMDbrcRbHZip2oQH4fgoq/DyN46iQEbLzdwr4Lsrv1cG8LeyEgN12965YTeyv263mbyYPtV2KLVkXO7aBztGL3+x8lUaH47f85ygP/s7ew37rJ3sL+497I6tzNtS3sc1ux57/l5Ng9WrGH5rZib/CvVuxleCWWyMn8LgSVe/fYW9in/GhvYd93gL2FvTPX7XUsin38judi7VPauxfFtrdiv30l1qqN5qLYRG7kHyEoRF4L+1+321vYDx6K7ISOj9fCPrcVe/5bTo5CYTj5r1bs/4FVG4Xsou0dt5xwUWwiz+DbISgEVNt/gmbKRKj27La3sB8+BtntOjxaC/u7WrHfvhL771bsT9tbsRer57gSa18UW+eiHSSix+WbISgE1D98h+CpE6H6Yz+spUojc+xE5LRtX2ALe5k5Nfdo7pjjlhOl4RgUOefyPlqmhDX4WVi15WEs2fSOVuzPshU7kRfyrRC02aDevBGaKZOgPHzI3sJ+8jTktIrNa2GfrxX7Hbec3KMVu31R7MrI0bTLuxIbXI6LYhP5EJkQQkhdxG3Xr2cW7hutVgRsWGfv4nzsKCzlnkFWz74wN6oKhfHUg1uxa6Py7q3LXSvWFhTOVuxEPqJ48ZD7vufdIWg2I2DNCmimTYTi9DlYy5WAtc2zkL2eDUXOiXu2YrdfgY10/JmLYhP5voJC0Gunw/LrpxFWtzJkl3KACAC9AMWr14AgBawBUcgJa+u45cS+KHYxqUsmIg/ktSFoC9DA+sbTsFR5Dua69XMbAERxUWwieiTePR0mInoIBU2H+QwWEfk1hiAR+TWGIBH5NYYgEfk1hiAR+TW33SJjs9kwfPhwHDt2DGq1GqNHj0ZERIS7hiciuie3HQlu27YNJpMJq1atQt++fTF+/Hh3DU1EdF9uC8H9+/ejWrVqAICKFSvir7/+ctfQRET35bYQ1Ov10N7Rw0+hUMBisbhreCKie3JbCGq1WhgMBsfXNpsNSqXXPrVHRD7CbSlUqVIl/Pzzz3j33Xfx559/IjLy7kXMC3q0hYjIFdz27PDtq8PHjx+HEAJjx47FM888446hiYjuy6MaKBARuRtvliYiv+ZVIXjgwAHExcXd9fpPP/2EZs2aoVWrVli9erVbx16yZAkaNmyIuLg4xMXF4Z9//nHquGazGUlJSYiNjUXz5s2RkpKS731X7/uDxnf1/lutVgwaNAitW7dG27Ztce7cuXzvu3r/HzS+q/cfAG7evIkaNWrg1KlT+V53x+99QeO7Y9+bNGni+PxBgwble2/16tVo2rQpWrZsiZ9//rnwgwgvMX/+fNGoUSPRokWLfK+bTCZRp04dkZaWJoxGo2jatKm4du2aW8YWQoi+ffuKQ4cOOXW8O61du1aMHj1aCCHErVu3RI0aNRzvuWPfCxpfCNfv/48//igGDhwohBDit99+E126dHG85479L2h8IVy//yaTSXz00UeiXr164uTJk/led/W+FzS+EK7f95ycHNG4ceN7vnft2jXRqFEjYTQaRUZGhuPPheE1R4Lh4eGYMWPGXa+fOnUK4eHhCA0NhVqtxquvvop9+/a5ZWwAOHz4MObPn482bdpg3rx5Th0XABo0aICePXs6vlYo8hZ/cse+FzQ+4Pr9r1OnDkaNGgUAuHTpEooVy1smwR37X9D4gOv3f8KECWjdujVKlCiR73V37HtB4wOu3/ejR48iOzsbCQkJaNeuHf7880/HewcPHsQrr7wCtVqNkJAQhIeH4+jRo4Uax2tCsH79+ve8r1Cv1yMkJO/WGo1GA71ef9d2rhgbABo2bIjhw4fjyy+/xP79+x/vsPweNBoNtFot9Ho9evTogV69ejnec8e+FzQ+4Pr9BwClUokBAwZg1KhRqF+/vuN1d+x/QeMDrt3/9evXIywszPGk1Z3cse8FjQ+4/mcfGBiIxMRELFq0CCNGjEC/fv0cD1g4c/+9JgTv5983YRsMhnx/Oa4khED79u0RFhYGtVqNGjVq4O+//3b6OJcvX0a7du3QuHFjxMTEOF53177fb3x37T9gPyL5/vvvMWTIEGRlZQFw78/+XuO7ev/XrVuHXbt2IS4uDkeOHMGAAQNw/fp1AO7Z94LGd8fPvmzZsnjvvfcgk8lQtmxZFClSxCX77/Uh+Mwzz+Ds2bNIS0uDyWTCvn378Morr7hlbL1ej0aNGsFgMEAIgT179uDFF1906hg3btxAQkICkpKS0Lx583zvuWPfCxrfHfu/YcMGx1QrKCgIMpnMMSV3x/4XNL6r93/ZsmVYunQpkpOT8fzzz2PChAkoXrw4APfse0Hju+Nnv3btWkejlatXr0Kv1zvGr1ChAvbv3w+j0YjMzEycOnXqng9gPAyvfW7t22+/RVZWFlq1aoWBAwciMTERQgg0a9YMJUuWdNvYvXv3Rrt27aBWq1GlShXUqFHDqWPNnTsXGRkZmD17NmbPng0AaNGiBbKzs92y7w8a39X7X69ePQwaNAht27aFxWLB4MGD8cMPP7jtZ/+g8V29//8m5e/9v8d39b43b94cgwYNQps2bSCTyTB27FgkJycjPDwctWvXRlxcHGJjYyGEQO/evREQEFCocXizNBH5Na+fDhMRPQ6GIBH5NYYgEfk1hiAR+TWGIBH5NYYgSWrnzp147733kJOTA8B+P1hMTAyuXr1617bz58/H22+/DaPRWOBnjhkzBpcuXXJJveR7eIsMSW7ixInIysrCJ598gvbt26Nbt26oWrXqXdvFxMSgSpUqiI6ORtOmTSWolHwRjwRJcr1798bhw4fx0Ucf4a233rpnAO7Zswfh4eFo3bo1li1bBgCwWCxo3bo1fv31V9y4cQMNGzbE5cuXERcXh1OnTmH//v1o2bIlYmNj0aVLF5c8V0zejyFIklOpVGjZsiV27dp13yO8NWvWoEWLFihXrhzUajUOHDgApVKJyZMnY8KECUhKSkL//v1RqlQpx/ds27YNdevWxdKlS9G8eXNkZGS4a5fIizAESXIXL17EwoULkZSUhKSkJGRmZjoaac6ZMwfp6enYsWMHvvrqKyQmJkKv12Pp0qUAgKeeegqVKlXCzZs3Ub169Xyf26VLF9y6dQvt27fHd999x9UN6Z54TpAkZTKZ0LZtW3Tv3h01atRAv379UKZMmXw9DJOTk3Hp0iUMGDAAAJCdnY3atWtj06ZNOHfuHCZMmIAXXngBpUuXRmJiIuLi4jB8+HDs3r0bb7zxBiIjIzFv3jyYzWZ0795dql0lD8V/GklSEyZMwKuvvup4+H748OFo2rQp3nzzTVSuXBmAfSo8ceJEx/cEBQWhXr16WL58ObZu3YqZM2eidOnSaNGiBd544w3Hdi+99BIGDhyI4OBgqFQqjBw50r07R16BR4JE5Nd4TpCI/BpDkIj8GkOQiPwaQ5CI/BpDkIj8GkOQiPwaQ5CI/BpDkIj82v8DevsVW7e42ZgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(x,x,color='orange',label=\"Linear\")\n",
    "plt.plot(x,x**2,color='red',label=\"Polynomial\")\n",
    "plt.xlabel(\"X-Axis\")\n",
    "plt.ylabel(\"Price\")\n",
    "plt.legend()\n",
    "plt.title(\"Scatter Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Matrix of Size (100,2) and scatter all the points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.random((20,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.61502399 0.93603131]\n",
      " [0.54678586 0.91550739]\n",
      " [0.95221716 0.89548109]\n",
      " [0.60326838 0.44693209]\n",
      " [0.29412804 0.66751364]\n",
      " [0.70573135 0.62398471]\n",
      " [0.659444   0.0141816 ]\n",
      " [0.56116366 0.32463432]\n",
      " [0.72448652 0.09429744]\n",
      " [0.42046047 0.43699048]\n",
      " [0.25361787 0.30046536]\n",
      " [0.60815152 0.64113342]\n",
      " [0.84508784 0.7830447 ]\n",
      " [0.42100965 0.71462299]\n",
      " [0.71018843 0.02607009]\n",
      " [0.23423287 0.6682592 ]\n",
      " [0.5548569  0.18427703]\n",
      " [0.34816158 0.00590052]\n",
      " [0.60492706 0.68498439]\n",
      " [0.21099226 0.87311957]]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0],X[:,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.bar?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar([1,2,4],[30,40,50],width=0.2,label='2018')\n",
    "plt.bar([1.2,3.2,4.2],[10,50,60],color='red',width=0.2,tick_label=[\"Iron\",\"Cal\",\"potassium\"],label='2018')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Downloading https://files.pythonhosted.org/packages/61/c7/f943fceb712579bc538700e2c157dc4972e16abfe29bd4969149bad98c74/pandas-0.24.2-cp37-cp37m-win_amd64.whl (9.0MB)\n",
      "Requirement already satisfied: python-dateutil>=2.5.0 in c:\\python\\lib\\site-packages (from pandas) (2.8.0)\n",
      "Collecting pytz>=2011k (from pandas)\n",
      "  Downloading https://files.pythonhosted.org/packages/3d/73/fe30c2daaaa0713420d0382b16fbb761409f532c56bdcc514bf7b6262bb6/pytz-2019.1-py2.py3-none-any.whl (510kB)\n",
      "Requirement already satisfied: numpy>=1.12.0 in c:\\python\\lib\\site-packages (from pandas) (1.16.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\python\\lib\\site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)\n",
      "Installing collected packages: pytz, pandas\n",
      "Successfully installed pandas-0.24.2 pytz-2019.1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are using pip version 19.0.3, however version 19.1.1 is available.\n",
      "You should consider upgrading via the 'python -m pip install --upgrade pip' command.\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"Dataset/movie_metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5043, 28)"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['color', 'director_name', 'num_critic_for_reviews', 'duration',\n",
       "       'director_facebook_likes', 'actor_3_facebook_likes', 'actor_2_name',\n",
       "       'actor_1_facebook_likes', 'gross', 'genres', 'actor_1_name',\n",
       "       'movie_title', 'num_voted_users', 'cast_total_facebook_likes',\n",
       "       'actor_3_name', 'facenumber_in_poster', 'plot_keywords',\n",
       "       'movie_imdb_link', 'num_user_for_reviews', 'language', 'country',\n",
       "       'content_rating', 'budget', 'title_year', 'actor_2_facebook_likes',\n",
       "       'imdb_score', 'aspect_ratio', 'movie_facebook_likes'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>color</th>\n",
       "      <th>director_name</th>\n",
       "      <th>num_critic_for_reviews</th>\n",
       "      <th>duration</th>\n",
       "      <th>director_facebook_likes</th>\n",
       "      <th>actor_3_facebook_likes</th>\n",
       "      <th>actor_2_name</th>\n",
       "      <th>actor_1_facebook_likes</th>\n",
       "      <th>gross</th>\n",
       "      <th>genres</th>\n",
       "      <th>...</th>\n",
       "      <th>num_user_for_reviews</th>\n",
       "      <th>language</th>\n",
       "      <th>country</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>budget</th>\n",
       "      <th>title_year</th>\n",
       "      <th>actor_2_facebook_likes</th>\n",
       "      <th>imdb_score</th>\n",
       "      <th>aspect_ratio</th>\n",
       "      <th>movie_facebook_likes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Color</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>723.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>855.0</td>\n",
       "      <td>Joel David Moore</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>760505847.0</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>936.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>1.78</td>\n",
       "      <td>33000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Color</td>\n",
       "      <td>Gore Verbinski</td>\n",
       "      <td>302.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>563.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Orlando Bloom</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>309404152.0</td>\n",
       "      <td>Action|Adventure|Fantasy</td>\n",
       "      <td>...</td>\n",
       "      <td>1238.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>300000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Mendes</td>\n",
       "      <td>602.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>Rory Kinnear</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>200074175.0</td>\n",
       "      <td>Action|Adventure|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>994.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>245000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>393.0</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2.35</td>\n",
       "      <td>85000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Color</td>\n",
       "      <td>Christopher Nolan</td>\n",
       "      <td>813.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22000.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>Christian Bale</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>448130642.0</td>\n",
       "      <td>Action|Thriller</td>\n",
       "      <td>...</td>\n",
       "      <td>2701.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>23000.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>164000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Doug Walker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rob Walker</td>\n",
       "      <td>131.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Color</td>\n",
       "      <td>Andrew Stanton</td>\n",
       "      <td>462.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>475.0</td>\n",
       "      <td>530.0</td>\n",
       "      <td>Samantha Morton</td>\n",
       "      <td>640.0</td>\n",
       "      <td>73058679.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>738.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>263700000.0</td>\n",
       "      <td>2012.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.35</td>\n",
       "      <td>24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Color</td>\n",
       "      <td>Sam Raimi</td>\n",
       "      <td>392.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>James Franco</td>\n",
       "      <td>24000.0</td>\n",
       "      <td>336530303.0</td>\n",
       "      <td>Action|Adventure|Romance</td>\n",
       "      <td>...</td>\n",
       "      <td>1902.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>258000000.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Color</td>\n",
       "      <td>Nathan Greno</td>\n",
       "      <td>324.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>Donna Murphy</td>\n",
       "      <td>799.0</td>\n",
       "      <td>200807262.0</td>\n",
       "      <td>Adventure|Animation|Comedy|Family|Fantasy|Musi...</td>\n",
       "      <td>...</td>\n",
       "      <td>387.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG</td>\n",
       "      <td>260000000.0</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>553.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.85</td>\n",
       "      <td>29000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Color</td>\n",
       "      <td>Joss Whedon</td>\n",
       "      <td>635.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19000.0</td>\n",
       "      <td>Robert Downey Jr.</td>\n",
       "      <td>26000.0</td>\n",
       "      <td>458991599.0</td>\n",
       "      <td>Action|Adventure|Sci-Fi</td>\n",
       "      <td>...</td>\n",
       "      <td>1117.0</td>\n",
       "      <td>English</td>\n",
       "      <td>USA</td>\n",
       "      <td>PG-13</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>21000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Color</td>\n",
       "      <td>David Yates</td>\n",
       "      <td>375.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>Daniel Radcliffe</td>\n",
       "      <td>25000.0</td>\n",
       "      <td>301956980.0</td>\n",
       "      <td>Adventure|Family|Fantasy|Mystery</td>\n",
       "      <td>...</td>\n",
       "      <td>973.0</td>\n",
       "      <td>English</td>\n",
       "      <td>UK</td>\n",
       "      <td>PG</td>\n",
       "      <td>250000000.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>11000.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2.35</td>\n",
       "      <td>10000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   color      director_name  num_critic_for_reviews  duration  \\\n",
       "0  Color      James Cameron                   723.0     178.0   \n",
       "1  Color     Gore Verbinski                   302.0     169.0   \n",
       "2  Color         Sam Mendes                   602.0     148.0   \n",
       "3  Color  Christopher Nolan                   813.0     164.0   \n",
       "4    NaN        Doug Walker                     NaN       NaN   \n",
       "5  Color     Andrew Stanton                   462.0     132.0   \n",
       "6  Color          Sam Raimi                   392.0     156.0   \n",
       "7  Color       Nathan Greno                   324.0     100.0   \n",
       "8  Color        Joss Whedon                   635.0     141.0   \n",
       "9  Color        David Yates                   375.0     153.0   \n",
       "\n",
       "   director_facebook_likes  actor_3_facebook_likes       actor_2_name  \\\n",
       "0                      0.0                   855.0   Joel David Moore   \n",
       "1                    563.0                  1000.0      Orlando Bloom   \n",
       "2                      0.0                   161.0       Rory Kinnear   \n",
       "3                  22000.0                 23000.0     Christian Bale   \n",
       "4                    131.0                     NaN         Rob Walker   \n",
       "5                    475.0                   530.0    Samantha Morton   \n",
       "6                      0.0                  4000.0       James Franco   \n",
       "7                     15.0                   284.0       Donna Murphy   \n",
       "8                      0.0                 19000.0  Robert Downey Jr.   \n",
       "9                    282.0                 10000.0   Daniel Radcliffe   \n",
       "\n",
       "   actor_1_facebook_likes        gross  \\\n",
       "0                  1000.0  760505847.0   \n",
       "1                 40000.0  309404152.0   \n",
       "2                 11000.0  200074175.0   \n",
       "3                 27000.0  448130642.0   \n",
       "4                   131.0          NaN   \n",
       "5                   640.0   73058679.0   \n",
       "6                 24000.0  336530303.0   \n",
       "7                   799.0  200807262.0   \n",
       "8                 26000.0  458991599.0   \n",
       "9                 25000.0  301956980.0   \n",
       "\n",
       "                                              genres  ...  \\\n",
       "0                    Action|Adventure|Fantasy|Sci-Fi  ...   \n",
       "1                           Action|Adventure|Fantasy  ...   \n",
       "2                          Action|Adventure|Thriller  ...   \n",
       "3                                    Action|Thriller  ...   \n",
       "4                                        Documentary  ...   \n",
       "5                            Action|Adventure|Sci-Fi  ...   \n",
       "6                           Action|Adventure|Romance  ...   \n",
       "7  Adventure|Animation|Comedy|Family|Fantasy|Musi...  ...   \n",
       "8                            Action|Adventure|Sci-Fi  ...   \n",
       "9                   Adventure|Family|Fantasy|Mystery  ...   \n",
       "\n",
       "  num_user_for_reviews language  country  content_rating       budget  \\\n",
       "0               3054.0  English      USA           PG-13  237000000.0   \n",
       "1               1238.0  English      USA           PG-13  300000000.0   \n",
       "2                994.0  English       UK           PG-13  245000000.0   \n",
       "3               2701.0  English      USA           PG-13  250000000.0   \n",
       "4                  NaN      NaN      NaN             NaN          NaN   \n",
       "5                738.0  English      USA           PG-13  263700000.0   \n",
       "6               1902.0  English      USA           PG-13  258000000.0   \n",
       "7                387.0  English      USA              PG  260000000.0   \n",
       "8               1117.0  English      USA           PG-13  250000000.0   \n",
       "9                973.0  English       UK              PG  250000000.0   \n",
       "\n",
       "   title_year actor_2_facebook_likes imdb_score  aspect_ratio  \\\n",
       "0      2009.0                  936.0        7.9          1.78   \n",
       "1      2007.0                 5000.0        7.1          2.35   \n",
       "2      2015.0                  393.0        6.8          2.35   \n",
       "3      2012.0                23000.0        8.5          2.35   \n",
       "4         NaN                   12.0        7.1           NaN   \n",
       "5      2012.0                  632.0        6.6          2.35   \n",
       "6      2007.0                11000.0        6.2          2.35   \n",
       "7      2010.0                  553.0        7.8          1.85   \n",
       "8      2015.0                21000.0        7.5          2.35   \n",
       "9      2009.0                11000.0        7.5          2.35   \n",
       "\n",
       "  movie_facebook_likes  \n",
       "0                33000  \n",
       "1                    0  \n",
       "2                85000  \n",
       "3               164000  \n",
       "4                    0  \n",
       "5                24000  \n",
       "6                    0  \n",
       "7                29000  \n",
       "8               118000  \n",
       "9                10000  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00e+00 5.63e+02 0.00e+00 2.20e+04 1.31e+02 4.75e+02 0.00e+00 1.50e+01\n",
      " 0.00e+00 2.82e+02]\n"
     ]
    }
   ],
   "source": [
    "likes = np.array(df['director_facebook_likes'])[:10]\n",
    "print(likes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['James Cameron' 'Gore Verbinski' 'Sam Mendes' 'Christopher Nolan'\n",
      " 'Doug Walker' 'Andrew Stanton' 'Sam Raimi' 'Nathan Greno' 'Joss Whedon'\n",
      " 'David Yates']\n"
     ]
    }
   ],
   "source": [
    "dir_names = np.array(df[\"director_name\"])[:10]\n",
    "print(dir_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,20))\n",
    "plt.bar(dir_names,likes,width=0.2,label='2018')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}